{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCntkxPqitOo",
        "outputId": "c910ca2e-5f3e-46d7-a3ab-b3f36aac62df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting bertopic\n",
            "  Downloading bertopic-0.15.0-py2.py3-none-any.whl (143 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.22.4)\n",
            "Collecting hdbscan>=0.8.29 (from bertopic)\n",
            "  Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting umap-learn>=0.5.0 (from bertopic)\n",
            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting sentence-transformers>=0.4.1 (from bertopic)\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "Collecting transformers<5.0.0,>=4.6.0 (from sentence-transformers>=0.4.1->bertopic)\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
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            "Collecting sentencepiece (from sentence-transformers>=0.4.1->bertopic)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting huggingface-hub>=0.4.0 (from sentence-transformers>=0.4.1->bertopic)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "Collecting pynndescent>=0.5 (from umap-learn>=0.5.0->bertopic)\n",
            "  Downloading pynndescent-0.5.10.tar.gz (1.1 MB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Building wheels for collected packages: hdbscan, sentence-transformers, umap-learn, pynndescent\n",
            "  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hdbscan: filename=hdbscan-0.8.33-cp310-cp310-linux_x86_64.whl size=3039184 sha256=b4365ff1f6d61b6df6558efbf3b99917c9d25f393415efd067b6cee472cd8722\n",
            "  Stored in directory: /root/.cache/pip/wheels/75/0b/3b/dc4f60b7cc455efaefb62883a7483e76f09d06ca81cf87d610\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125925 sha256=293a3e344477ce5b4f691d4825da1922bb9961b3067cf2d5bae1f02106d3f438\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82813 sha256=da8cb4015fd291f2552f9583fcfa016e17058aa7b0f38370b4e50f29e2400209\n",
            "  Stored in directory: /root/.cache/pip/wheels/a0/e8/c6/a37ea663620bd5200ea1ba0907ab3c217042c1d035ef606acc\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.10-py3-none-any.whl size=55621 sha256=55b19103eff6e2ddb0bdcbe7e4f08cc53ab143d4b174dc6bcbc4aae1dca6f4ff\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/38/5d/f60a40a66a9512b7e5e83517ebc2d1b42d857be97d135f1096\n",
            "Successfully built hdbscan sentence-transformers umap-learn pynndescent\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, huggingface-hub, transformers, pynndescent, hdbscan, umap-learn, sentence-transformers, bertopic\n",
            "Successfully installed bertopic-0.15.0 hdbscan-0.8.33 huggingface-hub-0.16.4 pynndescent-0.5.10 safetensors-0.3.1 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.31.0 umap-learn-0.5.3\n"
          ]
        }
      ],
      "source": [
        "! pip install bertopic\n",
        "from bertopic import BERTopic\n",
        "from umap import UMAP\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZXUGwiXTjec_"
      },
      "outputs": [],
      "source": [
        "all_text_clean = pd.read_excel(\"all_floyd_cleaned_final.xlsx\")\n",
        "dem_text_clean = pd.read_excel(\"dem_floyd_cleaned_final.xlsx\")\n",
        "rep_text_clean = pd.read_excel(\"rep_floyd_cleaned_final.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 465,
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        },
        "id": "zKixBX-llfk7",
        "outputId": "91c4e739-cec1-4878-ac4d-196cb4e6101d"
      },
      "outputs": [
        {
          "data": {
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          "metadata": {},
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        {
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        {
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        {
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        {
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          "metadata": {},
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        },
        {
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          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "6a5ffebc26a1467e8b0908148ec351d4",
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          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "18561135a44f4a43a4dc78c9c3e11b9f",
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          },
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        },
        {
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              "model_id": "5427b68077ce44fca99c7017480fc7d4",
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          },
          "metadata": {},
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        },
        {
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              "model_id": "c1c010ccc4d84854aac8df9032401507",
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          },
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        },
        {
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              "model_id": "d2d0c9b5baf8474eba94fe2412f4b730",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# Initiate UMAP\n",
        "umap_model = UMAP(n_neighbors=15,\n",
        "                  n_components=5,\n",
        "                  min_dist=0.0,\n",
        "                  metric='cosine',\n",
        "                  random_state=388)\n",
        "# Initiate BERTopic\n",
        "topic_model = BERTopic(umap_model=umap_model, language=\"english\", calculate_probabilities=True)\n",
        "# Run BERTopic model\n",
        "topics, probabilities = topic_model.fit_transform(all_text_clean['text_clean'].astype('str'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K3csFRAJktJE",
        "outputId": "6ea29cb2-ee31-4531-a60a-7ad941478a68"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "      <th>Representation</th>\n",
              "      <th>Representative_Docs</th>\n",
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              "  </thead>\n",
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              "      <td>5</td>\n",
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              "      <td>5_officer_involve_arrest_accountable</td>\n",
              "      <td>[officer, involve, arrest, accountable, hold, ...</td>\n",
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              "      <td>7_honor_memory_secure_ones</td>\n",
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              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>40</td>\n",
              "      <td>8_today_family_memorial_life</td>\n",
              "      <td>[today, family, memorial, life, funeral, love,...</td>\n",
              "      <td>[weeks ago know day funeral see worldwide dema...</td>\n",
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              "      <td>11</td>\n",
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              "      <td>11_change_vote_demand_real</td>\n",
              "      <td>[change, vote, demand, real, choice, speak, po...</td>\n",
              "      <td>[word wisdom want bring real change choice pol...</td>\n",
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              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>12_minneapolis_happen_people_death</td>\n",
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              "      <th>14</th>\n",
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              "      <td>29</td>\n",
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              "      <td>[tune, discuss, tv, ch, hall, live, talk, quin...</td>\n",
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              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>16_taylor_silence_breonna_ahmaud</td>\n",
              "      <td>[taylor, silence, breonna, ahmaud, arbery, hon...</td>\n",
              "      <td>[proud join colleagues morning moment silence ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>26</td>\n",
              "      <td>17_name_racism_murder_end</td>\n",
              "      <td>[name, racism, murder, end, action, need, pros...</td>\n",
              "      <td>[name murder need action end racism, name murd...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>25</td>\n",
              "      <td>18_men_black_murder_unarm</td>\n",
              "      <td>[men, black, murder, unarm, hand, video, man, ...</td>\n",
              "      <td>[die years ago death capture video black man g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>19_family_justice_sadness_tragic</td>\n",
              "      <td>[family, justice, sadness, tragic, tragedy, sw...</td>\n",
              "      <td>[thread community nation grapple death examine...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>20_nan___</td>\n",
              "      <td>[nan, , , , , , , , , ]</td>\n",
              "      <td>[nan, nan, nan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21_statement_death_read_visit</td>\n",
              "      <td>[statement, death, read, visit, weekend, elain...</td>\n",
              "      <td>[statement death, statement death, statement d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>22_lynch_senate_senator_floor</td>\n",
              "      <td>[lynch, senate, senator, floor, weep, antilync...</td>\n",
              "      <td>[republican senator stand way pass lynch feder...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>23</td>\n",
              "      <td>17</td>\n",
              "      <td>23_test_mask_participate_reopen</td>\n",
              "      <td>[test, mask, participate, reopen, wear, sit, s...</td>\n",
              "      <td>[participate days believe expose free walk-up ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>24</td>\n",
              "      <td>15</td>\n",
              "      <td>24_silence_caucus_moment_second</td>\n",
              "      <td>[silence, caucus, moment, second, democratic, ...</td>\n",
              "      <td>[min second silence senate democratic caucus h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>25_chauvin_derek_step_charge</td>\n",
              "      <td>[chauvin, derek, step, charge, arrest, officer...</td>\n",
              "      <td>[plead life police officer derek chauvin take ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>26</td>\n",
              "      <td>14</td>\n",
              "      <td>26_houston_houstonians_family_rest</td>\n",
              "      <td>[houston, houstonians, family, rest, thousands...</td>\n",
              "      <td>[honor knee mayor thousands people houston sho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>27_pass_help_know_plan</td>\n",
              "      <td>[pass, help, know, plan, thread, name, thank, ...</td>\n",
              "      <td>[help pass, help pass, help pass]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "      <td>28_tiananmen_square_china_party</td>\n",
              "      <td>[tiananmen, square, china, party, kong, hong, ...</td>\n",
              "      <td>[week anniversary tiananmen square massacre de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>29_subject_violation_profile_human</td>\n",
              "      <td>[subject, violation, profile, human, include, ...</td>\n",
              "      <td>[live black america include subject racial pro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>30_incredible_covid19_pour_experience</td>\n",
              "      <td>[incredible, covid19, pour, experience, salt, ...</td>\n",
              "      <td>[america experience time incredible sadness fi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>31_death_extent_investigate_responsible</td>\n",
              "      <td>[death, extent, investigate, responsible, thor...</td>\n",
              "      <td>[alive today senseless disturb death shake ame...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>32</td>\n",
              "      <td>10</td>\n",
              "      <td>32_minneapolis_autopsy_death_tragic</td>\n",
              "      <td>[minneapolis, autopsy, death, tragic, positive...</td>\n",
              "      <td>[real autopsy acknowledge truth cause death as...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "    Topic  Count                                     Name  \\\n",
              "0      -1    920         -1_justice_police_murder_officer   \n",
              "1       0    368      0_president_trump_military_peaceful   \n",
              "2       1    366           1_peaceful_right_violence_loot   \n",
              "3       2    143         2_police_officer_enforcement_law   \n",
              "4       3     98          3_black_racism_systemic_country   \n",
              "5       4     88           4_taylor_breonna_arbery_ahmaud   \n",
              "6       5     52     5_officer_involve_arrest_accountable   \n",
              "7       6     43                 6_min_derek_chauvin_knee   \n",
              "8       7     42               7_honor_memory_secure_ones   \n",
              "9       8     40             8_today_family_memorial_life   \n",
              "10      9     32      9_garner_eric_chokeholds_prevention   \n",
              "11     10     32           10_deserve_justice_step_family   \n",
              "12     11     30               11_change_vote_demand_real   \n",
              "13     12     30       12_minneapolis_happen_people_death   \n",
              "14     13     29                13_join_last_hope_silence   \n",
              "15     14     28                    14_tune_discuss_tv_ch   \n",
              "16     15     27           15_trump_million_dead_pandemic   \n",
              "17     16     26         16_taylor_silence_breonna_ahmaud   \n",
              "18     17     26                17_name_racism_murder_end   \n",
              "19     18     25                18_men_black_murder_unarm   \n",
              "20     19     25         19_family_justice_sadness_tragic   \n",
              "21     20     22                                20_nan___   \n",
              "22     21     21            21_statement_death_read_visit   \n",
              "23     22     18            22_lynch_senate_senator_floor   \n",
              "24     23     17          23_test_mask_participate_reopen   \n",
              "25     24     15          24_silence_caucus_moment_second   \n",
              "26     25     15             25_chauvin_derek_step_charge   \n",
              "27     26     14       26_houston_houstonians_family_rest   \n",
              "28     27     12                   27_pass_help_know_plan   \n",
              "29     28     12          28_tiananmen_square_china_party   \n",
              "30     29     11       29_subject_violation_profile_human   \n",
              "31     30     11    30_incredible_covid19_pour_experience   \n",
              "32     31     10  31_death_extent_investigate_responsible   \n",
              "33     32     10      32_minneapolis_autopsy_death_tragic   \n",
              "\n",
              "                                       Representation  \\\n",
              "0   [justice, police, murder, officer, black, dema...   \n",
              "1   [president, trump, military, peaceful, gas, te...   \n",
              "2   [peaceful, right, violence, loot, riot, peacef...   \n",
              "3   [police, officer, enforcement, law, reform, br...   \n",
              "4   [black, racism, systemic, country, death, amer...   \n",
              "5   [taylor, breonna, arbery, ahmaud, deaths, kill...   \n",
              "6   [officer, involve, arrest, accountable, hold, ...   \n",
              "7   [min, derek, chauvin, knee, neck, minutes, sec...   \n",
              "8   [honor, memory, secure, ones, day, countless, ...   \n",
              "9   [today, family, memorial, life, funeral, love,...   \n",
              "10  [garner, eric, chokeholds, prevention, excessi...   \n",
              "11  [deserve, justice, step, family, important, pr...   \n",
              "12  [change, vote, demand, real, choice, speak, po...   \n",
              "13  [minneapolis, happen, people, death, deserve, ...   \n",
              "14  [join, last, hope, silence, minutes, moment, s...   \n",
              "15  [tune, discuss, tv, ch, hall, live, talk, quin...   \n",
              "16  [trump, million, dead, pandemic, americans, pe...   \n",
              "17  [taylor, silence, breonna, ahmaud, arbery, hon...   \n",
              "18  [name, racism, murder, end, action, need, pros...   \n",
              "19  [men, black, murder, unarm, hand, video, man, ...   \n",
              "20  [family, justice, sadness, tragic, tragedy, sw...   \n",
              "21                            [nan, , , , , , , , , ]   \n",
              "22  [statement, death, read, visit, weekend, elain...   \n",
              "23  [lynch, senate, senator, floor, weep, antilync...   \n",
              "24  [test, mask, participate, reopen, wear, sit, s...   \n",
              "25  [silence, caucus, moment, second, democratic, ...   \n",
              "26  [chauvin, derek, step, charge, arrest, officer...   \n",
              "27  [houston, houstonians, family, rest, thousands...   \n",
              "28  [pass, help, know, plan, thread, name, thank, ...   \n",
              "29  [tiananmen, square, china, party, kong, hong, ...   \n",
              "30  [subject, violation, profile, human, include, ...   \n",
              "31  [incredible, covid19, pour, experience, salt, ...   \n",
              "32  [death, extent, investigate, responsible, thor...   \n",
              "33  [minneapolis, autopsy, death, tragic, positive...   \n",
              "\n",
              "                                  Representative_Docs  \n",
              "0   [bring life lose end grief family charge send ...  \n",
              "1   [federal law enforcement officer fire rubber b...  \n",
              "2   [nation hurt death outrage justice serve peace...  \n",
              "3   [legitimate law enforcement purpose saw case c...  \n",
              "4   [country long history slavery jim crow lynch s...  \n",
              "5   [recent kill ahmaud arbery breonna taylor demo...  \n",
              "6   [single officer involve hold accountable murde...  \n",
              "7   [derek chauvin hold knee neck minutes min seco...  \n",
              "8   [national day mourn prayers family love ones t...  \n",
              "9   [weeks ago know day funeral see worldwide dema...  \n",
              "10  [bring eric garner families step put end kill ...  \n",
              "11  [deserve justice deserve justice deserve justi...  \n",
              "12  [word wisdom want bring real change choice pol...  \n",
              "13  [thank call minneapolis injustice, happen happ...  \n",
              "14  [join americans country honor memory moment si...  \n",
              "15  [live tv alert come talk response riot country...  \n",
              "16  [trump call violence americans people country ...  \n",
              "17  [proud join colleagues morning moment silence ...  \n",
              "18  [name murder need action end racism, name murd...  \n",
              "19  [die years ago death capture video black man g...  \n",
              "20  [thread community nation grapple death examine...  \n",
              "21                                    [nan, nan, nan]  \n",
              "22  [statement death, statement death, statement d...  \n",
              "23  [republican senator stand way pass lynch feder...  \n",
              "24  [participate days believe expose free walk-up ...  \n",
              "25  [min second silence senate democratic caucus h...  \n",
              "26  [plead life police officer derek chauvin take ...  \n",
              "27  [honor knee mayor thousands people houston sho...  \n",
              "28                  [help pass, help pass, help pass]  \n",
              "29  [week anniversary tiananmen square massacre de...  \n",
              "30  [live black america include subject racial pro...  \n",
              "31  [america experience time incredible sadness fi...  \n",
              "32  [alive today senseless disturb death shake ame...  \n",
              "33  [real autopsy acknowledge truth cause death as...  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the list of topics\n",
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "m3VTAZfrnFWC",
        "outputId": "5364147a-939d-449e-afb1-a13bda94bb07"
      },
      "outputs": [
        {
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize top topic keywords\n",
        "topic_model.visualize_barchart(top_n_topics=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "BYZyJ6MDn9AG",
        "outputId": "307be1e4-50ab-43c2-dd76-f4fbacaa5e9a"
      },
      "outputs": [
        {
          "data": {
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize connections between topics using hierachical clustering\n",
        "topic_model.visualize_hierarchy(top_n_topics=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "SFUKm0Ylosj1",
        "outputId": "d74c4ad6-3769-4616-dea8-f154b224a7e5"
      },
      "outputs": [
        {
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>369</td>\n",
              "      <td>Kamala</td>\n",
              "      <td>Harris</td>\n",
              "      <td>D</td>\n",
              "      <td>803694179079458816</td>\n",
              "      <td>en</td>\n",
              "      <td>1265324956558163968</td>\n",
              "      <td>2020-05-26T16:52:32.000Z</td>\n",
              "      <td>We are fed up with Black lives being taken. Of...</td>\n",
              "      <td>We are fed up with Black lives being taken. Of...</td>\n",
              "      <td>We are fed up with Black lives being taken. Of...</td>\n",
              "      <td>We are fed up with Black lives being taken. Of...</td>\n",
              "      <td>We are fed up with Black lives being taken. Of...</td>\n",
              "      <td>We are fed up with Black lives being taken Off...</td>\n",
              "      <td>We are fed up with Black lives being taken Off...</td>\n",
              "      <td>we are fed up with black lives being taken off...</td>\n",
              "      <td>fed black lives taken officers held accountabl...</td>\n",
              "      <td>feed black live take officer hold accountable ...</td>\n",
              "      <td>-1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>15261</td>\n",
              "      <td>Justin</td>\n",
              "      <td>Amash</td>\n",
              "      <td>I</td>\n",
              "      <td>233842454</td>\n",
              "      <td>en</td>\n",
              "      <td>1265336582648847872</td>\n",
              "      <td>2020-05-26T17:38:44.000Z</td>\n",
              "      <td>George Floyd was murdered—callously and casual...</td>\n",
              "      <td>George Floyd was murdered—callously and casual...</td>\n",
              "      <td>George Floyd was murdered—callously and casual...</td>\n",
              "      <td>George Floyd was murdered callously and casual...</td>\n",
              "      <td>George Floyd was murdered callously and casual...</td>\n",
              "      <td>George Floyd was murdered callously and casual...</td>\n",
              "      <td>George Floyd was murdered callously and casual...</td>\n",
              "      <td>george floyd was murdered callously and casual...</td>\n",
              "      <td>murdered callously casually life meaning justice</td>\n",
              "      <td>murder callously casually life mean justice</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>11407</td>\n",
              "      <td>Eric</td>\n",
              "      <td>Swalwell</td>\n",
              "      <td>D</td>\n",
              "      <td>942156122</td>\n",
              "      <td>en</td>\n",
              "      <td>1265347363331342080</td>\n",
              "      <td>2020-05-26T18:21:35.000Z</td>\n",
              "      <td>This is uncomfortable to watch. Which is why y...</td>\n",
              "      <td>This is uncomfortable to watch. Which is why y...</td>\n",
              "      <td>This is uncomfortable to watch. Which is why y...</td>\n",
              "      <td>This is uncomfortable to watch. Which is why y...</td>\n",
              "      <td>This is uncomfortable to watch. Which is why y...</td>\n",
              "      <td>This is uncomfortable to watch Which is why yo...</td>\n",
              "      <td>This is uncomfortable to watch Which is why yo...</td>\n",
              "      <td>this is uncomfortable to watch which is why yo...</td>\n",
              "      <td>uncomfortable watch watch defend officer right...</td>\n",
              "      <td>uncomfortable watch watch defend officer right...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>11406</td>\n",
              "      <td>Eric</td>\n",
              "      <td>Swalwell</td>\n",
              "      <td>D</td>\n",
              "      <td>942156122</td>\n",
              "      <td>en</td>\n",
              "      <td>1265350036835241984</td>\n",
              "      <td>2020-05-26T18:32:12.000Z</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>It should not matter what your political party...</td>\n",
              "      <td>it should not matter what your political party...</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0 first_name last_name party           author_id  \\\n",
              "0             0         369     Kamala    Harris     D  803694179079458816   \n",
              "1             1       15261     Justin     Amash     I           233842454   \n",
              "2             2       11407       Eric  Swalwell     D           942156122   \n",
              "3             3       11406       Eric  Swalwell     D           942156122   \n",
              "4             4        3319    Sherrod     Brown     D            43910797   \n",
              "\n",
              "  lang                   id                created_at  \\\n",
              "0   en  1265324956558163968  2020-05-26T16:52:32.000Z   \n",
              "1   en  1265336582648847872  2020-05-26T17:38:44.000Z   \n",
              "2   en  1265347363331342080  2020-05-26T18:21:35.000Z   \n",
              "3   en  1265350036835241984  2020-05-26T18:32:12.000Z   \n",
              "4   en  1265358389208588032  2020-05-26T19:05:23.000Z   \n",
              "\n",
              "                                                text  \\\n",
              "0  We are fed up with Black lives being taken. Of...   \n",
              "1  George Floyd was murdered—callously and casual...   \n",
              "2  This is uncomfortable to watch. Which is why y...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand #JusticeForFloyd. How many more Black l...   \n",
              "\n",
              "                                         text_no_url  \\\n",
              "0  We are fed up with Black lives being taken. Of...   \n",
              "1  George Floyd was murdered—callously and casual...   \n",
              "2  This is uncomfortable to watch. Which is why y...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand #JusticeForFloyd. How many more Black l...   \n",
              "\n",
              "                                    text_no_username  \\\n",
              "0  We are fed up with Black lives being taken. Of...   \n",
              "1  George Floyd was murdered—callously and casual...   \n",
              "2  This is uncomfortable to watch. Which is why y...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand #JusticeForFloyd. How many more Black l...   \n",
              "\n",
              "                                      text_no_symbol  \\\n",
              "0  We are fed up with Black lives being taken. Of...   \n",
              "1  George Floyd was murdered callously and casual...   \n",
              "2  This is uncomfortable to watch. Which is why y...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand #JusticeForFloyd. How many more Black l...   \n",
              "\n",
              "                                     text_no_hashtag  \\\n",
              "0  We are fed up with Black lives being taken. Of...   \n",
              "1  George Floyd was murdered callously and casual...   \n",
              "2  This is uncomfortable to watch. Which is why y...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand. How many more Black lives must be take...   \n",
              "\n",
              "                                       text_no_punct  \\\n",
              "0  We are fed up with Black lives being taken Off...   \n",
              "1  George Floyd was murdered callously and casual...   \n",
              "2  This is uncomfortable to watch Which is why yo...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand How many more Black lives must be taken...   \n",
              "\n",
              "                                      text_no_number  \\\n",
              "0  We are fed up with Black lives being taken Off...   \n",
              "1  George Floyd was murdered callously and casual...   \n",
              "2  This is uncomfortable to watch Which is why yo...   \n",
              "3  It should not matter what your political party...   \n",
              "4  Demand How many more Black lives must be taken...   \n",
              "\n",
              "                                          text_lower  \\\n",
              "0  we are fed up with black lives being taken off...   \n",
              "1  george floyd was murdered callously and casual...   \n",
              "2  this is uncomfortable to watch which is why yo...   \n",
              "3  it should not matter what your political party...   \n",
              "4  demand how many more black lives must be taken...   \n",
              "\n",
              "                                    text_no_stopword  \\\n",
              "0  fed black lives taken officers held accountabl...   \n",
              "1   murdered callously casually life meaning justice   \n",
              "2  uncomfortable watch watch defend officer right...   \n",
              "3                    matter political party happened   \n",
              "4  demand black lives taken end country systemic ...   \n",
              "\n",
              "                                          text_clean  topic_prediction  \n",
              "0  feed black live take officer hold accountable ...                -1  \n",
              "1        murder callously casually life mean justice                -1  \n",
              "2  uncomfortable watch watch defend officer right...                -1  \n",
              "3                      matter political party happen                11  \n",
              "4  demand black live take end country systemic in...                -1  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the topic predictions\n",
        "topic_prediction = topic_model.topics_[:]\n",
        "# Save the predictions in the dataframe\n",
        "all_text_clean['topic_prediction'] = topic_prediction\n",
        "# Take a look at the data\n",
        "all_text_clean.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "zXtCffPrzKfk",
        "outputId": "f6153cda-9b4f-443a-e741-0f492fa818fc"
      },
      "outputs": [
        {
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              "          document.querySelector('#df-9ad3c266-eeac-449b-8c8e-773a826cebea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9ad3c266-eeac-449b-8c8e-773a826cebea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Topic  Frequency\n",
              "0      0        368\n",
              "1      1        366\n",
              "2      2        143\n",
              "3      3         98\n",
              "4      4         88\n",
              "5      5         52"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Subset the top 6 topics (Frequency > 50)\n",
        "text_top_topics = all_text_clean.loc[all_text_clean['topic_prediction'].isin([0,1,2,3,4,5])]\n",
        "# Frequency for each topic\n",
        "top_topic_freq = text_top_topics['topic_prediction'].value_counts()\n",
        "topic_freq = top_topic_freq.reset_index()\n",
        "topic_freq.columns = ['Topic', 'Frequency']\n",
        "topic_freq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "cZY3p-Bw1xR2",
        "outputId": "b5b8daa3-a609-4d5f-9419-241589a82d0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Topic', ylabel='Frequency'>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "# plot with seaborn barplot\n",
        "sns.set(font_scale=1)\n",
        "sns.set_style(\"white\")\n",
        "sns.barplot(data=topic_freq, x='Topic', y='Frequency', color='steelblue')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVj9QbDciaIb",
        "outputId": "9047687f-3413-4e12-d2f3-941b4c5b58d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "topic_prediction  party\n",
              "0                 D        348\n",
              "                  I          1\n",
              "                  R         19\n",
              "1                 D        161\n",
              "                  I          2\n",
              "                  R        203\n",
              "2                 D        100\n",
              "                  I          2\n",
              "                  R         41\n",
              "3                 D         90\n",
              "                  R          8\n",
              "4                 D         76\n",
              "                  R         12\n",
              "5                 D         40\n",
              "                  I          1\n",
              "                  R         11\n",
              "Name: topic_prediction, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_top_topics.groupby(['topic_prediction','party'])['topic_prediction'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Y9dR3dxU7gh6",
        "outputId": "6d2e7977-ff5f-4648-927d-7fb312ccb975"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a67f5bb7100>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "topic_count = pd.read_excel(\"topic_count.xlsx\")\n",
        "sns.set_palette(sns.color_palette([\"#4374B3\",\"#A62C2B\"]))\n",
        "ax = sns.barplot(x=\"Topic\", y=\"Frequency\", hue=\"Party\", data=topic_count, ci=None)\n",
        "for p,value in zip(ax.patches, topic_count['prop_party']):\n",
        "    ax.annotate(\"{:.2%}\".format(value), xy=(p.get_x()+p.get_width()/2, p.get_height()),\n",
        "                ha='center', va='bottom', fontsize=8)\n",
        "plt.legend(title=\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "74hQ98ZC3l2i",
        "outputId": "69325264-e561-4325-92de-eab27cd355e3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Run BERTopic model for Democrats\n",
        "topics, probabilities = topic_model.fit_transform(dem_text_clean['text_clean'].astype('str'))\n",
        "topic_model.visualize_barchart(top_n_topics=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "6iUhlXNiAYXs",
        "outputId": "abf07b6e-2673-4f74-ae46-afa00c7f542a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Topic  Frequency\n",
              "0      0        153\n",
              "1      1        130\n",
              "2      2        120\n",
              "3      3        115\n",
              "4      4         73\n",
              "5      5         71\n",
              "6      6         62\n",
              "7      7         43\n",
              "8      8         42\n",
              "9      9         37"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0090faa9-fc35-418a-89fe-ce5c7db110c2\">\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-a0d3214b-a922-46d5-b816-4fd9ab90da90\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0d3214b-a922-46d5-b816-4fd9ab90da90')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a0d3214b-a922-46d5-b816-4fd9ab90da90 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0090faa9-fc35-418a-89fe-ce5c7db110c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0090faa9-fc35-418a-89fe-ce5c7db110c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Get the topic predictions\n",
        "topic_prediction = topic_model.topics_[:]\n",
        "# Save the predictions in the dataframe\n",
        "dem_text_clean['topic_prediction'] = topic_prediction\n",
        "text_top_topics = dem_text_clean.loc[dem_text_clean['topic_prediction'].isin([0,1,2,3,4,5,6,7,8,9])]\n",
        "# Frequency for each topic\n",
        "top_topic_freq = text_top_topics['topic_prediction'].value_counts()\n",
        "topic_freq = top_topic_freq.reset_index()\n",
        "topic_freq.columns = ['Topic', 'Frequency']\n",
        "topic_freq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot with seaborn barplot\n",
        "sns.set(font_scale=1)\n",
        "sns.set_style(\"white\")\n",
        "sns.barplot(data=topic_freq, x='Topic', y='Frequency', color='steelblue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "AYTGJMvUOsKO",
        "outputId": "7859337c-2d04-450f-a080-722dd5c88a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Topic', ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "7fpcUtbU4CfN",
        "outputId": "833d77d0-58c8-4596-fef8-f38878714834"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Run BERTopic model for Republicans\n",
        "topics, probabilities = topic_model.fit_transform(rep_text_clean['text_clean'].astype('str'))\n",
        "topic_model.visualize_barchart(top_n_topics=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the topic predictions\n",
        "topic_prediction = topic_model.topics_[:]\n",
        "# Save the predictions in the dataframe\n",
        "rep_text_clean['topic_prediction'] = topic_prediction\n",
        "text_top_topics = rep_text_clean.loc[rep_text_clean['topic_prediction'].isin([0,1,2,3,4,5,6,7,8,9])]\n",
        "# Frequency for each topic\n",
        "top_topic_freq = text_top_topics['topic_prediction'].value_counts()\n",
        "topic_freq = top_topic_freq.reset_index()\n",
        "topic_freq.columns = ['Topic', 'Frequency']\n",
        "topic_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "mg3J8bqXCDUX",
        "outputId": "f4584e2c-8ec6-45c9-a9a7-19a0b9113d39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Topic  Frequency\n",
              "0      0         96\n",
              "1      1         57\n",
              "2      2         39\n",
              "3      3         32\n",
              "4      4         23\n",
              "5      5         20\n",
              "6      6         18\n",
              "7      7         18\n",
              "8      8         15\n",
              "9      9         14"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1613210e-8fa4-4442-bea6-b93767cec0bd\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2d1547a9-ec7a-4363-9fda-1bb2a5f429b3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2d1547a9-ec7a-4363-9fda-1bb2a5f429b3')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-2d1547a9-ec7a-4363-9fda-1bb2a5f429b3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1613210e-8fa4-4442-bea6-b93767cec0bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1613210e-8fa4-4442-bea6-b93767cec0bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot with seaborn barplot\n",
        "sns.set(font_scale=1)\n",
        "sns.set_style(\"white\")\n",
        "sns.barplot(data=topic_freq, x='Topic', y='Frequency', color='#A62C2B')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "-SEanwt9PJfJ",
        "outputId": "3502fdeb-6fe9-4869-804b-47891fdd3df9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Topic', ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
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